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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

Research on engine operation using hydrogen may enable appropriate optimization of thrust, and therefore performance, related to its potential use in aircraft. It is particularly important as the share of hydrogen in combustion affects the reduction of combustion products such as carbon dioxide, carbon monoxide, nitrogen oxide, hydrocarbons, and solid matter. This is in line with the new requirements regarding the increased supply of sustainable aviation fuels (SAFs) and the related changes in emissions, i.e., reducing the harmful impact of exhaust gases on the environment. This paper presents the results of measurements carried out in the GTM400 MOD turbojet engine. Based on the research performed, the impact of hydrogen and aviation kerosene combustion on selected engine parameters is presented. The paper shows changes in the rotational speed and volume flow of JET A-1 fuel as a function of engine operation time. Changes in temperature measured at the edge of the flame tube were also examined. The tests confirmed that the combustion chamber worked correctly in the selected area in the range of the tested fuel mixtures. After incorporating hydrogen into the combustion process, the consumption of traditional JET A-1 fuel was significantly reduced. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) with integrated battery energy storage systems (BESSs) are becoming crucial for modern power grids. This paper investigates the modeling and control of a grid-connected MMC-BESS, with a specific emphasis on state-of-charge (SoC) balancing. Compared to conventional hard arm SoC balancing control (HASBC), this paper proposes an alternative soft arm SoC balancing control (SASBC). The simulation results and analysis indicate the following: 1. SASBC provides superior performance in achieving SoC balance both between and within the arms, as compared to HASBC. 2. The MMC-BESS power fluctuates between phases, arms, and individual submodules to balance the SoC of batteries. After the accomplishment of SoC equalization, the power is equally distributed, and the circulating current is well eliminated. 3. MMC-BESS can operate in both the charging and discharging modes, and the total harmonic distortion (THD) of the output current is reduced from 6.80% to 1.13% after SoC balancing is achieved. 4. A robustness test shows the control system's effective performance in handling component variations. [ABSTRACT FROM AUTHOR]

A cascaded H-bridge multilevel converter topology is the ultimate solution for energy conversion in various industrial applications due to its exceptional features, such as high modularity and fault-tolerant capability. However, two circumstances can lead to unbalanced operation of the inverter, potentially causing a decrease in its reliability and survivability: unequal DC voltage sources and faulty cells. In recent decades, scientists and engineers have conducted intensive research and meaningful studies to propose control solutions capable of maintaining the stable and continuous operation of the inverter under these operational concerns. Typically, each challenge is addressed separately using a distinct compensation control scheme in the existing literature. The paper aims to offer a comprehensive review of the existing compensation control schemes appropriate for CHBMIs operating under unbalanced conditions. It overviews the most popular control schemes and summarizes their usefulness in such scenarios. The theoretical foundations of each control scheme are presented and discussed, including their operating principles, implementation schemes, advantages, and disadvantages. The paper concludes with suggested future trends that require further research for CHBMIs' continued growth and adoption in various industrial applications. [ABSTRACT FROM AUTHOR]

This paper presents the results of numerical simulations of heat transfer in a tube with a protective chromium layer on the inner surface made of steel, with different shrinkage temperatures. Shrinkage in the steel is an unfavorable phenomenon because it causes cracks in the chrome coating. The cracks contribute to the peeling of the protective material on the inner surface of the tube. Wear and damage to the chrome layer significantly shortens the tube's service life. The influence of the type of steel with medium carbon content on heat transfer for a sequence of ten, thirty, and sixty heat impulses was examined. Simulations were carried out for two selected steels with clearly different shrinkage temperatures, i.e., 30HN2MFA and X37CrMoV5-1 (1.2343). In 30HN2MFA steel, the shrinkage effect occurred at a temperature of approx. 750 °C, while in X37CrMoV5-1 steel it occurred at a temperature of approx. 870 °C. In the computational model, 30 cross-sections of the three-meter-long tube were analyzed. A time-dependent heat transfer coefficient was calculated in each zone. Heat transfer simulations were carried out using COMSOL version 6.1 software. This paper shows that for X37CrMoV5-1 steel, the shrinkage temperature on the inner surface of the tube was reached after approx. 60 heat impulses, while for 30HN2MFA steel, it was reached after approx. 30. The greatest differences in the number of impulses occurred for a pipe with a 200 µm thick chrome layer. [ABSTRACT FROM AUTHOR]

Reduction or elimination of reliance on traditional fossil fuels and of the emission of greenhouse gases and pollutants into the environment are affecting energy technologies, systems, and applications. In this context, one potential approach to achieving sustainability, decarbonization, and ensuring the energy and economic viability of existing and future energy systems involves adopting one or more renewable sources. The presented paper concentrates on examining the performance of a small-scale hybrid renewable polygeneration system. This system utilizes biogas produced through anaerobic digestion, which is then supplied to an internal combustion engine, along with solar energy converted into electrical energy by photovoltaic modules and wind energy harnessed through a wind turbine. A small-scale user, represented by residential buildings and a zootechnical farm with heating, cooling, and electrical energy demands, serves as the case study. TRNSYS software is employed to design and model the system, considering realistic assumptions about technical aspects and user energy requirements. The investigation involves analyzing the system's operation, considering both energy and economic perspectives. The paper discusses the pros and cons of combining biogas, solar, and wind energy in the proposed hybrid system under the considered case study. Despite non-satisfactory economic profitability without incentives, the proposed system allows one to save significant amounts of primary energy and carbon dioxide equivalent emissions. [ABSTRACT FROM AUTHOR]

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology's business schemes are investigated. Furthermore, the challenges faced by the V2G technology's business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology's research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT's global feature analysis and DenseNet161's dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines. [ABSTRACT FROM AUTHOR]

Since the power generation structures and load characteristics in each province in China are quite different, the distribution of peak regulating resources and demands are extremely imbalance. Restricted by a low power marketization degree, peak regulating resource shortages, and transmission channel blocks, the efficient utilization of new energy is facing greater pressures. In order to improve the mutual aid in regional power grids and to obtain more precise simulation results, this paper proposes a two-layer optimization dispatching model, considering the mutual aid of peak regulation resources between each province. It determines the optimal startup mode and the units' power output in each province and obtains the power output arrangements for all the units and the technical and economic indicators. The model and the solution method are original and innovative. And it effectively solved the unequal distribution problem between the peak regulating demands and resources of each provincial power grid. Finally, taking an actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the utilization rate of new energy, which verifies the effectiveness and feasibility of the proposed model and methods presented in this paper. [ABSTRACT FROM AUTHOR]

The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat. However, their efficiency remains limited. To enhance their efficiency, two distinct factors are suggested. First, the quality of materials plays a significant role in the development of more robust and efficient FCs. Second, the ability to identify, mitigate, and reduce the occurrence of faults through the use of robust control algorithms is crucial. Therefore, more focused on the second point, this paper compiles, distinguishes, and analyzes several publications from the past 25 years related to faults and their diagnostic techniques in FCs. Furthermore, the paper presents various schemes outlining different symptoms, their causes, and corresponding fault algorithms. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) offer key transformative potential from a societal point of view due to their modularity and ability to generate energy at the local level, allowing for the development of grassroots democratic and participatory initiatives. The paper aims to share insights into the processes of creating RES cooperatives in Poland. One of the first cooperatives to be established in the Upper Silesian region in Poland was the energy cooperative (EC) "Our Energy". This study presents an in-depth empirical analysis of a community-based renewable energy cooperative. The study employed a case study methodology, including a SWOT analysis framework, to describe the research subject and identify its strengths, weaknesses, opportunities, and threats. Key findings indicate that members benefit from stable energy prices and full recovery of the energy produced, and the cooperative is at the forefront of energy-sharing practices that minimize costs through direct transactions with the local municipality. The strategic goals of the EC focus on expanding membership, increasing the number of photovoltaic installations, implementing energy balancing, combating energy poverty, and reducing emissions. Challenges such as financial constraints and a lack of real-time monitoring of energy distribution are acknowledged, and carbon footprint reduction innovations and stakeholder engagement are highlighted as forward-looking approaches. The study highlights the role of cooperatives as a model for community-led sustainable energy initiatives. However, the study acknowledges the limitations of its small sample size, suggesting the need for broader research to understand the impact of collaborative energy on decarbonization. Future research directions are proposed, focusing on the long-term sustainability and socioeconomic impacts of energy cooperatives. This study contributes to the scholarly discourse on renewable energy cooperatives by offering insight into their potential to bridge the gap between energy producers and consumers and support sustainable community development. The main novelty of this paper lies in its detailed examination of a specific renewable energy cooperative, incorporating SWOT analysis, stakeholder perspectives, quantitative assessments, and a forward-thinking approach. This multifaceted analysis contributes to the existing literature on renewable energy initiatives, providing a valuable reference for researchers, policymakers, and practitioners in the field. [ABSTRACT FROM AUTHOR]

This paper deals with the electromagnetic characterization of a laminated toroidal ferromagnetic core made through additive manufacturing, specifically using the laser powder bed fusion process. The continuing demand for increasingly efficient, lightweight, and higher performance electric machines is creating huge challenges in the design and realization of new electric motor solutions. The constant improvements in additive manufacturing technologies have prompted researchers to investigate the possibility of adopting these production techniques for the manufacture of high-value electric motors. For these reasons, this paper investigates the ferromagnetic characteristics of an additively manufactured core made with FeSi6.5 powder. The BH curve and the specific iron losses of the processed material have been measured so that they can be compared with a commercial lamination, and have the possibility of carrying out more precise finite element simulations. [ABSTRACT FROM AUTHOR]

Assessing the performance of active balancing methods poses a significant challenge due to the time required to replicate the equalization of various balancing techniques under identical initial cell conditions. Conventional circuit simulation methods, designed for high-frequency switching behavior, impose a considerable computational burden when applied to the long-term equalization of battery cells. To address this challenge, this paper presents an efficient performance evaluation method employing an average equivalent model of the equalizers. By representing the charge transfer mechanism inherent to the equalization process, the proposed approach is compatible with the most widely used switched-energy-tank equalizers. The validity of this method is confirmed through simulation and experimental results. In the case of four series-connected battery cells, our proposed approach can assess the performance of a three-hour equalization process in just one minute of execution time. The use cases in the paper highlight the practical feasibility of the AM in facilitating performance comparisons of SET-Es under various initial conditions. [ABSTRACT FROM AUTHOR]

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management. [ABSTRACT FROM AUTHOR]

This paper presents a simulation model for a system equipped with thermoelectric elements, considering the impact of independent thermal contact resistances on each side of the module. An analytical model was constructed, taking into account the asymmetry of thermal resistances between the generator/cooler and the respective heat source/heat sink. A comparative analysis of thermoelectric device operating indicators such as conducted heat, efficiency, and the electricity/cooling power produced was performed. The selection of electrical current in the circuit was analysed based on the maximization of power or efficiency. This paper discusses deviations from ideal conditions, specifically the absence of thermal resistance between the heat source/sink and the thermoelectric junction. The model accurately simulates the operating conditions of the thermoelectric system with a low computational cost. The results indicate that the total thermal resistance, rather than its location, predominantly affects the operation of the thermoelectric generator. However, in cooling operations, the influence of thermal resistance significantly depends on the cooling power demand and temperature. [ABSTRACT FROM AUTHOR]

The sustainable implementation of resources and the transition to low-carbon construction is on the world's daily agenda. This paper describes the development of criteria for the efficient management of material resources in low-carbon construction. Net income, gross profit, balance sheets, and equity are proposed as indicators that serve as criteria for assessing the efficient use of resources. Nonlinear regression models were the methodological basis for the establishment of cause-and-effect relationships of the volume of construction of transport structures. It was established that since construction companies do not purchase materials for storage, the cost of materials for use in low-carbon technological construction work is directly proportional to the cost of construction. The volume of production in the construction industry is determined by construction costs. More efficient use of low-carbon materials reduces cost and construction waste. In this paper, we have established the relationship between the indicator of efficiency for the functioning of the construction enterprises in Ukraine and the use of low-carbon materials for the construction of transport structures. The practical value of the obtained results for low-carbon construction management lies in proving the relationship between the performance indicators of the construction enterprise (net profit, gross profit, output volume, balance sheets, and equity capital) and the use of low-carbon material resources. Our results form the basis for future research into the use of a cost-based model for low-carbon economy transition in municipalities and regions. [ABSTRACT FROM AUTHOR]

The essential role of Information and Communication Technologies (ICT) in modern electricity grids makes it necessary to consider them when evaluating the scalability and replicability capabilities of smart grid systems. This paper proposes a novel step-by-step methodology to quantitatively perform an ICT scalability and replicability analysis (SRA) in a smart grid context. The methodology is validated and exemplified by applying it to two real case studies that are demonstrated in the EU-funded RESPONSE project and comprise solutions relying on different communication technologies. The results of the proposed methodology are summarised through ICT scalability and replicability maps, which are introduced in this paper as a quick way of obtaining an overview of the scalability and replicability capabilities of an ICT system and as an efficient way of estimating the feasibility of scenarios not covered in the SRA. [ABSTRACT FROM AUTHOR]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

Despite their key role in integrated waste management, direct (incineration) and indirect (gasification/pyrolysis) waste combustion processes are still opposed by some of the general public due to the past emission levels of air pollutants. In fact, although the release of air pollutants (especially dioxin) to the atmosphere from waste combustion processes has gradually decreased over the years, thanks to the introduction of stricter regulations and more advanced removal technologies, there is still an unsolved problem regarding the public acceptance of waste-to-energy facilities. The aim of this paper is to provide an overview of the state-of-the-art air pollution control (APC) technologies used in waste combustion facilities. Air pollution control technologies are designed to reduce or eliminate the emissions of harmful pollutants into the atmosphere. These technologies are important for safeguarding public health, protecting ecosystems, complying with regulations, and promoting a more sustainable and resilient future for both local and global communities. This paper will highlight the complexity behind emission control and the efforts made by this sector over the years. This paper will also propose suggested configurations based on the interactions/complementarity between different APC technologies and recent findings to improve their performance. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

The paper presents a proposed system that supplies a 400 Hz single-phase onboard grid from the DC onboard bus. This system enables independent compensation of reactive power in the AC grid. Independent control of active and reactive power flow requires the decomposition of current in the grid into active and reactive components. Independent control of active and reactive power requires the use of synchronizers that operate in the dq frame system. If synchronization is performed with a single-phase grid, the transformation of dq requires the virtual quadrature signals. Standard quadrature signal generation systems use a second-order generalized integrator. To improve the dynamics of the system, the paper proposes a new quadrature generator that operates on the basis of trigonometric calculations instead of a second-order integration system. The developed system was implemented in a proportional-resonant current control system. Tests carried out in steady state and in dynamic states related to typical grid disturbances proved significantly better dynamic properties than those of a standard integrator-based system. [ABSTRACT FROM AUTHOR]

The EU Carbon Border Adjustment Mechanism (CBAM), which is regarded as the EU's key policy tool to address carbon leakage, might have a non-negligible impact on China's exports, as China is an important trading partner for the EU's carbon-intensive products. This paper uses the GTAP-E model to simulate the impact of the EU CBAM on China's exports to the EU from four aspects, export price, trade structure, trade value and terms of trade, by setting up multiple scenarios. The results show that the EU CBAM reduces the export prices of China's taxed sectors to the EU, and that the export prices of other sectors show the same change characteristics. The export volume of China's taxed sectors decreases differently with the export transfer effect and export inhibition effect. In terms of trade value, the EU carbon tariffs not only reduce China's export value but also lead to a reduction in EU exports. The implementation of the EU CBAM improves the terms of trade of the EU and worsens the terms of trade of China. An expansion of the scope of taxation and a change in the calculation method of carbon emissions would aggravate the change in the terms of trade. The results suggest that feasible measures should be taken to strengthen international cooperation, promote the construction of a unified national carbon market and export diversification, and establish a firm carbon emission accounting system in order to mitigate the negative impact of the EU CBAM. [ABSTRACT FROM AUTHOR]

The complexity and strong spatial and temporal characteristics of municipal solid waste (MSW) have made resource utilization a major challenge in establishing the life-cycle model of MSW. Based on the planning of the domestic "dual-carbon" target and the current status of the structural transformation of resource utilization, this paper summarizes the physicochemical properties of MSW in China by component, species, and region. The aim is to identify the physicochemical components of MSW in different regions of China, drawing on the research findings of various scholars. A total of 159 sets of MSW data were collected, including 90 sets of physical composition and 69 sets of elemental composition. These data were used to calculate the calorific value of MSW and determine the energy-recovery and power-generation potentials before and after MSW classification. The analysis estimates the volume of MSW requiring removal in different regions of China in 2021 and assesses the effectiveness of the energy-recovery potential (ERP) and power-generation potential (PGP) before and after MSW classification in these regions. The aim is to offer insightful guidance and recommendations for municipal waste-treatment strategies tailored to the diverse regions of China. [ABSTRACT FROM AUTHOR]

The electrification of road transport is developing dynamically around the world. Many automotive companies are introducing electric vehicles to the market, and their popularity is constantly growing. The increasing popularity of electric vehicles is caused by individual countries' governments encouraging people to switch to electric vehicles and their lower operating costs. In 2022, the number of electric vehicles in China will exceed 10 million. Europe and the USA rank second and third in global electric car stock, respectively. The number of available electric vehicle models is constantly growing, remaining approximately 2.5 times smaller than the case of vehicles with an internal combustion engine. Among others, a significant limitation to the popularity of electric cars is users' fear of range and the density of the charging infrastructure network. This paper presents the objectives regarding public areas and charging stations around the European Union's comprehensive and core transport network. It is worth noting that the vehicle and charging point's charging connectors vary depending on the geographical region. Therefore, the currently used charging connectors for different regions are presented. Charging time depends significantly on the charging current, the power of the charging point, and the devices installed in the vehicle. The paper analyzes the limitations of charging power resulting from the onboard charger's power and the charging point's power. It presents the charging time of selected electric vehicles. The second aspect that is also the subject of user concerns and discussed in this article is issues related to the safety of electric vehicles. General safety indicators of such vehicles based on Euro-NCAP tests are characterized. Attention was also paid to more detailed problems related to active and passive safety and functional safety analyses. The issue of the fire hazard of electric vehicles was discussed together with modern experiences regarding post-accident procedures in the event of fires. [ABSTRACT FROM AUTHOR]

The use of the common mode current for stator winding insulation condition assessment has been extensively studied. Two main approaches have been followed. The first models the electric behavior of ground-wall insulation as an equivalent R C circuit; these methods have been successfully applied to high-voltage high-power machines. The second uses the high frequency of the common mode current which results from the voltage pulses applied by the inverter. This approach has mainly been studied for the case of low-voltage, inverter-fed machines, and has not yet reached the level of maturity of the first. One fact noticed after a literature review is that in most cases, the faults being detected were induced by connecting external elements between winding and stator magnetic core. This paper presents a case study on the use of the high-frequency common mode current to monitor the stator insulation condition. Insulation degradation occurred progressively with the machine operating normally; no exogenous elements were added. Signal processing able to detect the degradation at early stages will be discussed. [ABSTRACT FROM AUTHOR]

Metallic electrodes are widely used in many applications, the analysis of their frequency-domain behavior is an important subject, particularly in applications related to earthing/grounding systems, from dc up into the MHz range. In this paper, a numerical evaluation of the frequency-dependent complex impedance of the hemispherical ground electrode is implemented. A closed-form solution for non-zero frequencies is still a difficult task to achieve as evidenced in a previous paper dedicated to the subject and, therefore, numerical approaches should be an alternative option. The aim of this article is to present a solution based on a numerical method using finite element analysis. In typical commercial FE tools, electric currents exhibit azimuthal orientation and, as such, the magnetic field has a null azimuthal component but non-null axial and radial components. On the contrary, a dual problem is considered in this work, with a purely azimuthal magnetic field. To overcome the difficulty of directly using a commercial FE tool, a novel formulation is developed. An innovative 2D formulation, the ι-form, is developed as a modification of the H-formulation applied to axisymmetric magnetic field problems. The results are validated using a classical 3D H-formulation; comparisons showed very good agreement. The electrode complex impedance is analyzed considering two different cases. Firstly, the grounding system is constituted by a hemispherical electrode surrounded by a remote concentric electrode; in the second case, the grounding system is constituted by two identical thin hemispherical electrodes. Computed results are presented and discussed, showing how the grounding impedance depends on the frequency and, also, on the radius of the remote concentric electrode (first case) or on the distance between the two hemispherical electrodes (second case). [ABSTRACT FROM AUTHOR]

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa's ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kW pk /taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country's dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%. [ABSTRACT FROM AUTHOR]

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval's theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination ( R 2 ) higher than the other models, and it has the smallest mean absolute percentage error ( M A P E ) and root mean square error ( R M S E ). [ABSTRACT FROM AUTHOR]

In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research on cross-working-condition fault prediction. An improved transfer learning (TL) model based on a temporal convolutional network (TCN) is proposed to solve the problem of low fault prediction accuracy across operating conditions. First, the peak emitter voltage of an IGBT aging dataset is selected as the source domain failure characteristic, and the TCN model is trained after the removal of outliers and noise reduction. Then, the time–frequency features are extracted according to the characteristics of the target domain data, and the target domain representation data are obtained using kernel principal component analysis (KPCA) for dimensionality reduction. Finally, the TCN model trained on the source domain is transferred; the model is fine-tuned according to the target domain data, and the learning rate, the number of hidden layer nodes, and the number of training times in the network model are optimized using the dung beetle optimization (DBO) algorithm to obtain the optimal network, making it more suitable for target sample fault prediction. The prediction results of this TCN model, the long short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the recursive neural network (RNN) model are compared and analyzed by selecting prediction performance evaluation indexes. The results show that the TCN model has a better predictive effect. Comparing the prediction results of the TCN-based optimized transfer learning model with those of the directly trained TCN model, the mean square error, root mean square error, and mean absolute error are reduced by a factor of two to three, which provides an effective solution for fault prediction across operating conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the deterioration of, and the abnormal temperature rise in, the GFRP core rod material of compact V-string composite insulators subjected to prolonged alternating flexural loading under wind-induced stresses. The axial stress on the GFRP (Glass Fiber Reinforced Plastic) core rod, resulting from transverse wind loads, is a focal point of examination. By establishing a stress model and damage model, the paper simulates and computes the evolution of damage in the outer arc material of composite insulator core rods subjected to alternating flexural loads. Additionally, a multi-factor coupled aging platform is set up, integrating humidity, heat, and mechanical stress, to simulate the crazing deterioration process of composite insulators under alternating flexural loads. Experimental results reveal that during 400,000 alternating load cycles, the core rod underwent stages of surface damage, damage increasing, fatigue embrittlement, matrix hydrolysis, and fiber fracture. Simultaneously, the silicone rubber sheath on the outer side of the composite insulator's bending arc develops cracks over aging time, creating pathways for moisture ingress into the interface and core rod. The dielectric constant and dielectric loss factor of the aging region of the core rod increase to varying degrees compared to the non-aging part. Moreover, the degree of abnormal heating of the samples intensifies with the duration of aging experiments. These findings underscore the significance of understanding the aging and decay-like fracture process of compact line V-string composite insulators. They provide crucial insights for future research aimed at enhancing the material properties of composite insulator core rods. [ABSTRACT FROM AUTHOR]

The distribution of renewable energy sources is geographically limited. In the process of long-distance transmission, the direct current flowing from a ground electrode into the ground will cause a higher step voltage, which will bring serious security risks to the surrounding industry and life. Accurate calculation of the complex soil electrical model around the grounding electrode is crucial for site selection. Existing simulation software like CDEGS results in significant errors, particularly in complex karst topography. Therefore, constructing a finite element model that accurately reflects the characteristics of geotechnical soil near the DC grounding electrode is an essential but unresolved problem. This paper establishes a soil electrical model for karst topography and explores the impact of cave-type caverns and underground rivers on the step voltage distribution of DC grounding electrodes. These research findings can guide the site selection of DC transmission projects in karst topography. [ABSTRACT FROM AUTHOR]

In recent years, several research works have addressed and developed the phase-locked loop (PLL) in single-phase grid-connected converters with different structures and properties. Each has merits and demerits, such as a complex structure, high computational burden, and slow transient response. This paper aims to comprehensively review advanced single-phase PLLs based on transport delay operators to realize signal orthogonality. A deep insight into the PLLs' small-signal modeling, main characteristics, stability analysis, and loop filter design are provided in this paper. The main advantages and drawbacks are explained for each type of PLL in terms of different performance indexes, such as settling time, estimation error, and ripples in the estimated grid information. This paper also aims to provide optimal tuning and design of the loop filter gains from the large-signal model point of view, including all the nonlinearities, adopting the stochastic optimization method. All simulations are implemented using the MATLAB/Simulink 2018b environment to validate all theoretical analyses of this paper. The sampling and nominal frequencies are set to be 100 kHz and 50 Hz throughout all the simulation studies. [ABSTRACT FROM AUTHOR]

The reduction in GHG emissions has become one of the most important objectives in Europe. One of these clean technologies in the quest for large-scale decarbonisation is the blending of hydrogen and natural gas (H2NG). The significance of this article lies in conducting a technical–economic analysis that relies on simulating annual consumption patterns. This study focuses on the utilization of H2NG at different proportions within a distribution network in Bucharest, Romania's largest city. The paper is a fallow up of another paper and presents a case study for the injection through all four entry points in the city of different percentages of hydrogen and the dynamic of the mix through the gas distribution network. The study found that the variation in H2 in the network is high and depends on the entry point levels and consumption. An economic analysis was conducted on seasonal real consumption, which confirmed the investment's profitability. [ABSTRACT FROM AUTHOR]

This paper proposes a simple design scheme for a modular NPC inverter using thermal RC network analysis. The proposed design process is an efficient and straightforward approach to designing the heatsink for a 300 kW modular neutral-point-clamped inverter. The heatsink design plays a crucial role in achieving high power density of a power converter because the weight and size of the heatsink are primarily influenced by its type. The structure and dimensions of the heatsink are mainly determined based on the generated heat by losses of the power semiconductor switches. In this paper, a thermal RC network model was established using parameters from the power switch module and was applied to the simulation of the power converter. The thermal losses of the power semiconductor switches were calculated via this process, and the heatsink was designed according to the calculated thermal losses. The proposed design scheme was analyzed and compared with the thermal fluid dynamic model. To validate the feasibility of the proposed design process, The simulation results were compared with experimental results. [ABSTRACT FROM AUTHOR]

In response to global energy demand, the enhancement of transport capacity in electrical transmission lines is deemed essential. The conventional method of constructing new lines is considered costly, time-consuming, and subject to constraints imposed by economic and environmental factors. Among various emerging solutions aimed at enhancing the passive capacity of transmission lines, the technology of flexible AC transmission systems (FACTS) has been demonstrated to be highly effective. This paper proposes the utilization of the static synchronous series compensator (SSSC), a closed-loop control system and a type of FACTS, to alleviate the overloading of transmission lines in the Syrian electrical network. To achieve this objective, the network is first modeled to identify the overloaded transmission lines. Subsequently, the particle swarm optimization (PSO) algorithm is employed for the optimal sizing and allocation of SSSCs within the network. The findings showcase the significant reduction in loads on critically overloaded transmission lines subsequent to the successful implementation of SSSC devices. This serves to validate the improvements made to the existing infrastructure of the Syrian electrical network's transmission lines, without necessitating the construction of new transmission lines. [ABSTRACT FROM AUTHOR]

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly 'black box' models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions. [ABSTRACT FROM AUTHOR]

Reliable heat and power supply are among the basic household needs nowadays. It is especially topical in rural or distant locations that may be cut off from the energy grid due to extreme weather or other events. Nonetheless, the sustainability of our power production systems has to be considered to sustain our planet's long-term abilities to provide energy resources and a viable environment. Renewable energy resources must be prioritized in rural and remote areas, simultaneously strengthening distributed production ability and self-sufficiency. In this context, the use of the Stirling engine for heat to power generation in households, on the scale of small communities, and by using only renewable resources is becoming increasingly topical. Therefore, this research aims to identify the current state-of-the-art for Stirling engine applications using biomass as a renewable energy source, in the context of sustainability and energy security. The paper summarizes the current research tendencies at the household level in the use of biomass-based Stirling engines for renewable heat and power generation in decentralized energy systems. The methodology applied is a structured literature review and content analysis. The research results corroborate the progress towards the use of renewable resources and towards increased energy efficiency because the keyword and overlay analysis showed more frequent publishing in these areas. The content analysis on two more specific sub-directions of interest, i.e., biomass-fuelled Stirling engine use in households and Stirling engine implications on energy security, showed that though research activity in these directions has increased lately, more profound research is needed, especially on aspects of energy security and independence. The content analysis revealed a lack of in-depth analysis on the effects of Stirling engine use on energy security or energy independence, which is suggested as a topical subject for future research. [ABSTRACT FROM AUTHOR]

The AC battery utilizing second-life time batteries has gained great interest currently with the advantages of both power solutions and economic benefits. In this system, the power converters play a crucial role in the stable and effective operation of the system. This paper focused on the AC/DC stage with the chosen topology being the interleaved full bridge (IFB) converter due to its flexibility and the ability to increase the power rate of the system. For the sake of high-performance operation, various pulse width modulation (PWM) methods for this converter are analyzed. First, based on the theory of the traditional PWM methods for a full bridge inverter in combination with the interleaved technique, this paper proposed three interleaved PWM methods for the IFB converter. Secondly, the proposed methods are theoretically compared in terms of the output current, common-mode voltage, and power losses. Finally, the evaluation is carried out by both the simulation and the experimental prototype, in which the results are in good agreement with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices' contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol's time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G. [ABSTRACT FROM AUTHOR]

The interaction of water waves with floating bodies can be modeled with linear potential flow theory and numerically solved with the boundary element method (BEM). This method requires the construction of dense matrices and the resolution of the corresponding linear systems. The cost of this method in terms of time and memory grows at least quadratically with the size of the mesh, and the resolution of large problems (such as large farms of wave energy converters) can, thus, be very costly. Approximating some blocks of the matrix with data-sparse matrices can limit this cost. While matrix compression with low-rank blocks has become a standard tool in the larger BEM community, the present paper provides its first application (to our knowledge) to linear potential flows. In this paper, we assess how efficiently low-rank blocks can approximate interaction matrices between distant meshes when using the Green function of linear potential flow. Due to the complexity of this Green function, a theoretical study is difficult, and numerical experiments are used to test the approximation method. Typical results on large arrays of floating bodies show that 99% of the accuracy can be reached with 10% of the coefficients of the matrix. [ABSTRACT FROM AUTHOR]

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues. [ABSTRACT FROM AUTHOR]

This paper's research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car's charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time. [ABSTRACT FROM AUTHOR]

As part of the intergovernmental and public interventions to reduce carbon dioxide emissions, there are no existing regulations to ban the sale of petrol motorcycles (PM), but it is expected that motorcycle regulations will follow car regulations with several years of delay. There is an emerging trend in motorcycle uptake, which will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfies design challenges specific to electric motorcycles (EM) and electric powertrains. There is significant importance in motorcycle design to quantify the vehicle-level performance indicators and specifications, which are not limited to total vehicle mass, range, acceleration performance, and top speed. Those performance indicators should be quantified for different powertrain configurations and component selections to identify the most suitable configuration for the specific motorcycle development. In this paper, an innovative powertrain sizing approach is proposed to provide solutions for EMs against the design challenges specific to electric motorcycles. The innovative approach is to apply the practice of design space exploration (DSE) in resilient system design (RSD) to EM development. As a proof of concept, a case study of battery sizing is presented, in which a powertrain sizing tool is used to identify battery pack sizing requirements using requirement-based design (RBD), sensitivity analysis and DSE. The case study shows that the RBD approach allows EM product developers to identify a single solution, while DSE clearly demonstrates the trade-off between different configurations, taking multiple design variables into account. The tool prioritises high accessibility and high confidence with limited information at the early phases of electric motorcycle powertrain component sizing and selection. [ABSTRACT FROM AUTHOR]

With the continuous development of large-scale wind and photovoltaic power worldwide, the net load fluctuation of systems is increasing, thereby imposing higher demands for power supply assurance and new energy consumption capacity within emerging power systems. It is imperative to establish a quantifiable and efficient model for planning new power systems, to propose an analytical approach for determining optimal evolutionary paths, and to advance research on flexible resource planning across wide areas. In this paper, based on the simplified operating characteristics of multi-type flexible resources, a source-grid-load-storage collaborative planning and evolution analysis framework is established. Secondly, the lowest total cost of the whole life cycle of the system is taken as the optimization goal, the multiple flexible resource investment decisions and production operation constraints of various flexible resources on all sides of the system are considered, and the source-grid-load-storage planning model is established. Then, through the investment decision-making strategy setting of the system in different planning level years, the evolutionary path analysis method of the whole life cycle economy and weighted environmental protection benefit of the system is given. Finally, by taking the sending-end power grid in Gansu Province as an example, a case study is carried out. Calculations of new energy, key channels within the province, energy storage capacity, and load-side response capacity requirements for 2025, 2030, and 2060 are optimized. Based on the above analysis, the optimal evolution path of the power grid is proposed. When considering the weighted benefits of economy and environmental protection, the greater the weight of environmental protection benefits, the greater the possibility of choosing a radical scheme. The model and method proposed in this paper can provide technical reference for the future development planning and evolution analysis of new power systems. [ABSTRACT FROM AUTHOR]

Short-term wind power forecasting has difficult problems due to the very large variety of speeds of the wind, which is a key factor in producing energy. From the point of view of the whole country, an important problem is predicting the total impact of wind power's contribution to the country's energy demands for succeeding days. Accordingly, efficient planning of classical power sources may be made for the next day. This paper will investigate this direction of research. Based on historical data, a few neural network predictors will be combined into an ensemble that is responsible for the next day's wind power generation. The problem is difficult since wind farms are distributed in large regions of the country, where different wind conditions exist. Moreover, the information on wind speed is not available. This paper proposes and compares different structures of an ensemble combined from three neural networks. The best accuracy has been obtained with the application of an MLP combiner. The results of numerical experiments have shown a significant reduction in prediction errors compared to the naïve approach. The improvement in results with this naïve solution is close to two in the one-day-ahead prediction task. [ABSTRACT FROM AUTHOR]

It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem is presented. First, statistical simulation methods to identify critical nodes of very large-scale smart grid infrastructure based on the topological features of embedding networks are developed, and then a linear integer programming model based on generalized assignment problem (GAP) for the redundancy allocation of critical nodes in smart grid infrastructure is presented. This paper aims to contribute to the field by employing a general redundancy allocation problem (GRAP) model from high-order nonlinear to linear model transformation. The model is specifically implemented in the context of smart grid infrastructure. The innovative linear integer programming model proposed in this paper capitalizes on the logarithmic multiplication property to reframe the inherently nonlinear resource allocation problem (RAP) into a linearly separable function. This reformulation markedly streamlines the problem, enhancing its suitability for efficient and effective solutions. The findings demonstrate that the combined approach of statistical simulation and optimization effectively addresses the size limitations inherent in a sole optimization approach. Notably, the optimal solutions for redundancy allocation in large grid systems highlight that the cost of redundancy is only a fraction of the economic losses incurred due to weather-related outages. [ABSTRACT FROM AUTHOR]

This paper reports on the linear colorimetric and electrochromic (EC) characteristics of electrodeposited polyaniline (PANI) films. This paper also investigates the infrared EC properties of acid-doped PANI films. The electrochemical polymerization method was employed to create a porous and thin PANI film layer onto PET-ITO substrates. This layer was capped with WO3 film to create a gel electrolyte sandwich structure that demonstrates the compatibility of PANI films with cathodic WO3 films in full devices. The electrodeposition of the film was fabricated by applying different voltages and time, with the optimal film quality achieved with the 1.7 V voltage and a 20 min deposition period. The surface morphology, optical performance, electrochemical behavior, and molecular structure evolution are comprehensively studied in this work. The linear colorimetric behaviors and the corresponding significant changes in the structure in Raman spectra build direct strong quantitative relations in EC polymers. The well-defined oxidation and reduction peaks observed in the cyclic voltammetry indicate the ion-diffusion dominant process in the electrochromism of PANI. Significant transitions between the benzene and quinone phases in the Raman spectra are found in the bleached and colored states of polymers. This study enhances the understanding of PANI film structure and electrochemical and associated optical properties, providing more insights into the dual-function EC charge storage polymers and other energy-related functional materials. [ABSTRACT FROM AUTHOR]

Bifacial photovoltaic (BPV) panels represent one of the main solar technologies that will be used in the near future for renewable energy production, with a foreseen market share in 2030 of 70% among all the photovoltaic (PV) technologies. Compared to monofacial panels, bifaciality can ensure a gain in energy production per unit panel area together with a competitive cost. However, it is of paramount importance to identify whether there is also an environmental benefit when adopting bifacial technologies as opposed to traditional monofacial ones. To obtain a proper insight into the environmental impact, this paper reviews the Life Cycle Assessment (LCA) studies of bifacial solar panels, identifying the most crucial processes and materials that raise environmental burdens. The analysis also contributes to determining whether the major aspects that influence energy production in real operation scenarios and, most of all, that can ensure the gain associated with bifaciality, are considered and how these can further affect the overall environmental impacts. In this sense, it was found that the installation parameters like the mounting structure, or the choice of ground material to raise the albedo as well as the diffuse irradiation that hits the rear surface of thepanel, are commonly not considered during LCA analysis. However, none of the analyzed studies address the issue in a comprehensive way, hampering an effective comparison between both the different works and traditional monofacial PV panels. Recommendations for future LCAs are finally proposed. [ABSTRACT FROM AUTHOR]

Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes. [ABSTRACT FROM AUTHOR]